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All Journal Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Prosiding Seminar Nasional Sains Dan Teknologi Fakultas Teknik Prosiding SNATIF Infotech Journal IKRA-ITH Informatika : Jurnal Komputer dan Informatika JURIKOM (Jurnal Riset Komputer) JUMANJI (Jurnal Masyarakat Informatika Unjani) Jurnal Tekno Kompak Jurnal ICT : Information Communication & Technology SENSITEK Jurnal Informatika dan Rekayasa Elektronik JATI (Jurnal Mahasiswa Teknik Informatika) Jurnal Teknik Informatika (JUTIF) Jurnal Abdimas Kartika Wijayakusuma International Journal Software Engineering and Computer Science (IJSECS) Journal of Informatics and Communication Technology (JICT) Prosiding Seminar Nasional Sisfotek (Sistem Informasi dan Teknologi Informasi) Jurnal Algoritma Innovative: Journal Of Social Science Research RESLAJ: Religion Education Social Laa Roiba Journal Journal of Informatics and Communication Technology (JICT)
Rezki Yuniarti
Universitas Jenderal Achmad Yani
Religion Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Law, Crime, Criminology & Criminal Justice Mathematics Public Health Social Sciences Other

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Pengembangan Aplikasi Multimedia untuk Edukasi Sejarah pada Zaman Megalitikum Di Indonesia pada Platform Mobile Ihsan, Aminuddin; Yuniarti, Rezki; Ilyas, Ridwan
Jurnal Algoritma Vol 22 No 2 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-2.2522

Abstract

This research developed a two-dimensional (2D) educational game themed on the history of the Megalithic Age in Indonesia for mobile platforms using the Rapid Prototyping method and Unity Engine. The aim was to provide interactive learning media that combines historical narratives with exploration-based game mechanics and puzzle solving. The development process included visual asset design, prototyping, and alpha and beta testing. Beta testing involved 20 respondents from junior high and high school levels, resulting in a satisfaction rate of 71.5% (Agree category) based on the Likert Scale, which indicates positive acceptance of the gameplay and educational content. The limitations of the study include the scope of historical material, which only covers the early Megalithic period, the limited number of respondents, and the difficulty level adjustment, which is not yet optimal for all age groups. Further development is recommended to expand the variety of missions, enrich historical content with the latest research references, and add analytical features to monitor user learning achievements.
Analisis Sentimen Terhadap Ulasan Aplikasi Deepseek AI Menggunakan Model Bidirectional LSTM dan IndoBert Mahendra, Lucky Syahroni; Herry Chrisnanto, Yulison; Yuniarti, Rezki
Jurnal Algoritma Vol 22 No 2 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-2.2950

Abstract

Advancements in Natural Language Processing (NLP) technology have progressed rapidly, marked by the emergence of various Large Language Models (LLMs) such as ChatGPT, Gemini, and DeepSeek AI. One particularly popular model is DeepSeek AI due to its ability to understand and respond to natural language text more contextually. The increasing popularity of this application is accompanied by a growing number of user reviews, which serve as an important source of data for capturing their experiences and perceptions. This study aims to analyze user sentiment toward the DeepSeek AI application using a deep learning approach. Specifically, the research focuses on evaluating the performance of sentiment classification models in the context of Indonesian-language data, which is relatively limited and imbalanced. The dataset was collected from user reviews on the Google Play Store and categorized into three sentiment classes: positive, negative, and neutral. The method employed is a combination of IndoBERT and Bidirectional Long Short-Term Memory (BiLSTM). IndoBERT is used to generate contextual text representations in Indonesian, while BiLSTM is utilized to recognize sequential word patterns. Experimental results show that this hybrid model achieves an accuracy of 45%, with the highest F1-score of 0.66 in the positive class. Meanwhile, a macro-average F1-score of 0.33 and a ROC-AUC of 0.546 indicate that the model’s performance remains limited in distinguishing the three classes evenly. Nevertheless, the main contribution of this study lies in the development of a new dataset consisting of 1,774 Indonesian-language reviews related to LLM-based applications, which can be used for further research in the field of Natural Language Processing (NLP). The study also demonstrates the effectiveness of integrating IndoBERT and BiLSTM for sentiment analysis of Indonesian text with imbalanced data distribution.
Co-Authors Adhinata Kusuma, Rendy Aditya Setiawan Putra Agus Komamrudin Agus Komarudin Agus Komarudin Agus Komarudin Agus Komarudin Alvi Yudistira Aminuddin Ihsan, Aminuddin Angga Kersana Munggaran Azhari, Anissa Dewi, Liony Puspita Dony Liany Esmeralda C Djamal Esmeralda C. Djamal Esmeralda C. Djamal Esmeralda Contesa Djamal Esmeralda Contessa Djamal Fajri Rakhmat Umbara Ferry Kurniawan Fitaloka, Intan Gumilar, Desta Gumilar, Desta Gunawan Abdillah Hadiana, Asep Id Iman Muhdi Kamal, Angga Mochamad Kemal Musthafa Rajabi Khanif Azhar Waluyo Komamrudin, Agus Komarudin, Agus Kresna Septyana Ekaputra Kurniawan, Alexander Tommy Liany, Dony Luthfia Oktasari Mahendra, Lucky Syahroni Moh Khoerul Imam Muhammad Dwi Laksono Nenza Nurfirmansyah Nugraha, Mochammad Ganjar Nugroho, Akbar Satrio Putra, Eddie Krishna Rahmat Ryan Adhitya Ramdani, Rahmat Muhamad Rasyid Ibnu Prasetyo Rd Muhammad Alfajri Ridwan Ilyas Riyan Firmansyah Selia Aysiah Singgih, Dimas Tacbir Hendro Pudjiantoro Teguh Munawar Ahmad Wage Rizal Solichin Wildah Fatma Lestari Wina Witanti Wina Witanti Wini Afrilia Yulison Herry Chrisnanto